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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Teknotan: Jurnal Industri Teknologi Pertanian Bulletin of Electrical Engineering and Informatics PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic AGRIEKONOMIKA Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Kultivasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Agrotek Tropika Journal of Tropical Crop Science Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Community Services Cel International Journal of Applied Mathematics, Sciences, and Technology for National Defense Jurnal Pengabdian Masyarakat Nasional Journal Collabits
Rahmat Budiarto
Department Of Agronomy, Faculty Of Agriculture, Universitas Padjadjaran, Sumedang, 45363, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

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The Effects of Preharvest Mild Shading on the Quality and Production of Essential Oil from Kaffir Lime Leaves (Citrus hystrix) Budiarto, Rahmat; Poerwanto, Roedhy; Santosa, Edi; Efendi, Darda; Agusta, Andria
Journal of Tropical Crop Science Vol. 9 No. 01 (2022): Journal of Tropical Crop Science
Publisher : Department of Agronomy and Horticulture, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jtcs.9.01.15-21

Abstract

Kaffir lime (Citrus hystrix DC) is a less popular citrus species commonly used as a food spice and a source of essential oil. Early studies report the success of preharvest mild shading to increase leaf yield, although there is still limited information on the effect of preharvest shading on the quality of essential oil produced. The aim of this current study is to evaluate the effect of preharvest mild shading factors on the yield, physical characteristics, and metabolite fingerprinting of kaffir lime leaves essential oil (KLLEO). One-year-old kaffir lime trees were sampled in two preharvest treatments, i.e., open sun and mild shading (24% light reduction) at Pasir Kuda experimental field, Bogor, Indonesia. Statistical analysis showed that there was no significant effect of preharvest treatment on yield and physical characteristics (color, specific gravity, and refractive index) of KLLEO. In contrast, there was a metabolite fingerprinting variation of KLLEO as an effect of mild shading. The relative percentage of bergamol, citronellol, caryophyllene oxide, citronellic acid, isopulegol, isopulegyl formate, limonene, linalool, and linalool oxide was increased by mild shading. On the other hand, the main metabolite (citronellal) was significantly reduced by about 10% in shading treatment, as compared to the open-sun ones.
Citrus is a Multivitamin Treasure Trove: A Review Budiarto, Rahmat; Mubarok, Syariful; Nursuhud, Nursuhud; Rahmat, Bayu Pradana Nur
Journal of Tropical Crop Science Vol. 10 No. 01 (2023): Journal of Tropical Crop Science
Publisher : Department of Agronomy and Horticulture, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jtcs.10.1.57-70

Abstract

Citrus is popularly known as the source of beneficial and essential nutrients for human health, including vitamins. The current review revealed the content of multivitamins, not only vitamin C but also vitamins A, B, and E that are not widely acknowledged within Citrus. Numerous Citrus genotypes contain vitamin C, with the grapefruit (Citrus paradisi) being the richest, and citron (C. medica) the poorest. Vitamin A in the form of β-carotene, α-carotene, and β-cryptoxanthin is commonly found within Citrus, especially in several colored flesh species such as grapefruit, mandarin (C. reticulate), and orange (C. sinensis). In terms of vitamin B, orange and grapefruit are proven to contain B-complex, including thiamine (B1), riboflavin (B2), niacin (B3), pantothenic acid (B5), pyridoxine (B6), biotin (B7), inositol (B8) and folate (B9). Vitamin E in the form of α-tocopherol was detected in leaf kaffir lime (C. hystrix) and orange (C. sinensis), lemon (C. limon), mandarin (C. reticulate), and tangerine (C. nobilis) fruit. This review summarizes the nutritional content of Citrus; Citrus contains not only vitamin C but also other vitamins beneficial to human health, therefore Citrus consumption is highly recommended.
Yield and Physicochemical Characteristics of Kaffir Lime Leaf Essential Oils Subjected to Different Post-Harvest Treatment Budiarto, Rahmat; Poerwanto, Roedhy; Santosa, Edi; Efendi, Darda; Agusta, Andria; Rofiq, Muhamad Abdul
Journal of Tropical Crop Science Vol. 11 No. 02 (2024): Journal of Tropical Crop Science
Publisher : Department of Agronomy and Horticulture, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jtcs.11.02.97-104

Abstract

The importance of kaffir lime leaf as essential oils (EOs) raw material is starting to get attention because of its commercial value; however, there is no quality reference for kaffir lime leaf EOs, especially in response to various post-harvest handlings. This study aimed to describe the physicochemical characteristics and yield of kaffir lime EOs subjected to different post-harvest. Bogor originated-kaffir lime leaf was prepared to be subjected to several post-harvest treatments, i.e., control/fresh green leaves (P1); milling to produce green leaf flour (P2); drying to produce dry brown leaf (P3), and milling and drying to produce brown leaf flour (P4). The result showed that post-harvest treatment generally decreases an oil yield and increases darkness color, specific gravity and refractive index of tested EOs. Additionally, post-harvest treatment also changes metabolite profile revealed by GCMS analysis. The relative percentage of caryophyllene and citronellol tends to increase, while the linalool and citronellal levels decrease due to tested post-harvest treatment. It was implied that for the benefit of the fragrance industry with a high citronellal requirement, EOs should be made from fresh green leaves and the leaves should be avoided from drying and powdering treatment.
Evaluasi Pengujian Keamanan Arsitektur Zero Trust Network pada Jaringan Smart Home untuk Mengatasi Serangan Data Sniffing Mugianto, Dwi Rizki; Budiarto, Rahmat
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i11.16898

Abstract

Keamanan jaringan Smart Home menjadi perhatian utama dalam menghadapi ancaman serangan data sniffing yang semakin meningkat. Penelitian ini bertujuan untuk mengevaluasi pengujian keamanan arsitektur Zero Trust Network pada jaringan Smart Home guna mengatasi risiko serangan data sniffing. Pendekatan Zero Trust Network diterapkan untuk meminimalkan potensi kerentanan sistem, dengan mendasarkan kepercayaan pada pengguna dan perangkat secara individual, bahkan dalam lingkungan internal. Metode penelitian melibatkan simulasi serangan data sniffing terhadap jaringan Smart Home yang diimplementasikan dengan arsitektur Zero Trust Network. Hasil penelitian ini diharapkan dapat memberikan wawasan mendalam tentang keamanan jaringan Smart Home dengan pendekatan Zero Trust Network, termasuk identifikasi potensi celah keamanan yang perlu diperbaiki. Penelitian ini memberikan kontribusi dalam pengembangan strategi keamanan yang lebih efektif untuk melindungi data sensitif di lingkungan Smart Home, menjadikannya lebih tahan terhadap serangan data sniffing, dan memberikan landasan untuk pengembangan lebih lanjut dalam mengamankan infrastruktur IoT di masa depan.
Clustering man in the middle attack on chain and graph-based blockchain in internet of things network using k-means Nuzulastri, Sari; Stiawan, Deris; Satria, Hadipurnawan; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p176-185

Abstract

Network security on internet of things (IoT) devices in the IoT development process may open rooms for hackers and other problems if not properly protected, particularly in the addition of internet connectivity to computing device systems that are interrelated in transferring data automatically over the network. This study implements network detection on IoT network security resembles security systems from man in the middle (MITM) attacks on blockchains. Security systems that exist on blockchains are decentralized and have peer to peer characteristics which are categorized into several parts based on the type of architecture that suits their use cases such as blockchain chain based and graph based. This study uses the principal component analysis (PCA) to extract features from the transaction data processing on the blockchain process and produces 9 features before the k-means algorithm with the elbow technique was used for classifying the types of MITM attacks on IoT networks and comparing the types of blockchain chain-based and graph-based architectures in the form of visualizations as well. Experimental results show 97.16% of normal data and 2.84% of MITM attack data were observed.
Implementation of automation configuration of enterprise networks as software defined network Prasetyo, Lindo; Prihandi, Ifan; Rifqi, Muhammad; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p99-111

Abstract

Software defined network (SDN) is a new computer network configuration concept in which the data plane and control plane are separated. In Cisco system, the SDN concept is implemented in Cisco Application Centric Infrastructure (Cisco ACI), which by default can be configured through the main controller, namely the Application Policy Infrastructure Controller (APIC). Conventional configuration on Cisco ACI creates problems, i.e.: the large number of required configurations causes the increase of time required for configuration and the risk of misconfiguration due to repetitive works. This problem reduces the productivity of network engineers in managing Cisco system. In overcoming these problems, this research work proposes an automation tool for Cisco ACI configuration using Ansible and Python as an SDN implementation for optimizing enterprise network configuration. The SDN is implemented and experimented at PT. NTT Indonesia Technology network, as a case study. The experimental result shows the proposed SDN successfully performs multiple routers configurations accurately and automatically. Observations on manual configuration takes 50 minutes and automatic configuration takes 6 minutes, thus, the proposed SDN achieves 833.33% improvement.
Machine learning-based anomaly detection for smart home networks under adversarial attack Rejito, Juli; Stiawan, Deris; Alshaflut, Ahmed; Budiarto, Rahmat
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p122-129

Abstract

As smart home networks become more widespread and complex, they are capable of providing users with a wide range of applications and services. At the same time, the networks are also vulnerable to attack from malicious adversaries who can take advantage of the weaknesses in the network's devices and protocols. Detection of anomalies is an effective way to identify and mitigate these attacks; however, it requires a high degree of accuracy and reliability. This paper proposes an anomaly detection method based on machine learning (ML) that can provide a robust and reliable solution for the detection of anomalies in smart home networks under adversarial attack. The proposed method uses network traffic data of the UNSW-NB15 and IoT-23 datasets to extract relevant features and trains a supervised classifier to differentiate between normal and abnormal behaviors. To assess the performance and reliability of the proposed method, four types of adversarial attack methods: evasion, poisoning, exploration, and exploitation are implemented. The results of extensive experiments demonstrate that the proposed method is highly accurate and reliable in detecting anomalies, as well as being resilient to a variety of types of attacks with average accuracy of 97.5% and recall of 96%.
Implementation of Banana Cultivation and Post-Harvest Technology in Wanasari Village, Purwakarta Mubarok, Syariful; Budiarto, Rahmat; Rufaidah, Fathi; Mutiara, Pipit; Suminar, Erni; Yani, Yanyan Mochamad
Indonesian Journal of Community Services Cel Vol. 5 No. 1 (2026): Indonesian Journal of Community Services Cel
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70110/ijcsc.v5i1.131

Abstract

Background: Bananas are among the most important horticultural commodities in Indonesia. In West Java, Purwakarta is a center of banana production. Farmers rely on traditional propagation and production methods, resulting in low yields and productivity. The main problem for banana farmers is the lack of seed availability, where almost no farmer used seed from tissue culture and only 10% of the respondent did not know about post-harvest processing technology.Aims: The aim of this activity is to improve the skills of farmers in the propagation of banana plants by in vitro culture, banana production, and post-harvest technology. This activity took place in Wanasari, Purwakarta City.Methods: The methodology for these activities was to deliver lectures and conduct practice sessions with farmers on banana propagation, production, and postharvest technology. Additionally, we provided them with a banana plant from in vitro culture.Result: The community service activity showed that the farmers were highly interested and enthusiastic about the technology introduced to them with increasing the post-test score of those audiences. The participants' enthusiasm and confidence in implementing banana cultivation and post-harvest techniques demonstrate the effectiveness and practicality of the training methods in supporting banana cultivation. This work is expected to empower farmers to manage their gardens and post-harvest banana handling, thereby contributing to economic resilience and food security.
Deep Learning-Based Autism Detection Using Facial Images and EfficientNet-B3 Hasanudin, Muhaimin; Afiyati, Afiyati; Budiarto, Rahmat; Wahab, Abdi; Jokonowo, Bambang; Indrianto, Indrianto; Yosrita, Efy; Hanifah, Nurul Afif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4574

Abstract

This study presents a novel deep learning approach for early detection of Autism Spectrum Disorder (ASD) using facial image analysis. Leveraging the EfficientNet-B3 model, the research addresses limitations in traditional diagnostic methods by autonomously extracting discriminative facial features associated with ASD. A balanced dataset of 2,940 facial images (1,470 autistic and 1,470 non-autistic children) from Kaggle was pre-processed to 200x200 pixels and evaluated under three dataset-splitting scenarios (80:10:10, 70:15:15, and 60:20:20) to assess generalisability. The model, trained with the Adam optimiser over 10 epochs, achieved optimal performance in the 80:10:10 scenario, with 84.67% precision, 84.35% recall, and 84.32% F1 score. Results demonstrate high confidence (>90% probability) in distinguishing autistic from non-autistic individuals on unseen data. The study underscores the potential of integrating deep learning into clinical decision-support systems for ASD detection, offering a robust, scalable, and efficient solution to improve diagnostic accuracy and reduce reliance on manual methods.
Co-Authors Abdi Wahab Abdullakasim, Supatida Adi Hermansyah, Adi Aditya Pradana Afiyati, Afiyati Ahmad Heryanto, Ahmad Ahmed, Ali Siraj Al Aufa, Elfa Muhammad Ihsan Ali Firdaus Alshaflut, Ahmed ANDRIA AGUSTA Anne Nuraini Anni Yuniarti Anto Saputra, Iwan Pahendra Audrey, Berby Febriana Azka Ghafara Putra Agung Bambang Jokonowo Bedine Kerim, Bedine Bin Idris, Mohd Yazid Deris Stiawan Dikdik Kurnia Dwi Budi Santoso Dwinanda, Syahvan Rifqi Edi Santosa Efendi, Darda Efy Yosrita, Efy Envry Artanti Duidahayu Putri Erik Setiawan Ermatita - Erni Suminar Ezura, Hiroshi Fadlan Atalla Muhammad Fajri, Hauzan Ariq Musyaffa Fakhrudin, Zidan Al Buqhori Fakhrurroja, Hanif Farida Farida Farida Fauziah, Rossita Fiky Yulianto Wicaksono Firnando, Rici Firstina Iswari Ghorbanpour, Mansour Giyarto, Gunes Hadipurnawan Satria Hanifah, Nurul Afif Harjunadi Wicaksono, Harjunadi Haryanto, Yoyon Hauzan Ariq Musyaffa Fajri Hayane Adeline Warganegara, Hayane Adeline Helvi Yanfika Idris, Mohd Yazid Bin Iman Saladin B. Azhar Indah Listiana Indrianto Indrianto Iswari, Firstina Jajang Sauman Hamdani Jatmika, Muhammad O. Juli Rejito Kemahyanto Exaudi Komala, Mega Kus Hendarto, Kus Kusumadewi, Vira Kusumiyati Kusumiyati Luciana Djaya, Luciana M. Miftakul Amin Maolana, Adrian Mochamad Arief Soleh Mohamed Shenify Mohd Yazid Idris Mohd Yazid Idris Mohd. Yazid Idris Mugianto, Dwi Rizki Muhaimin Hasanudin Muhammad Afif Muhammad Rifqi Muhammad Rizki Muhammad, Fadlan Atalla Mutiara, Pipit Nisa, Kahirun Noor Istifadah Nursuhud Nursuhud Nuzulastri, Sari Osman, Mohd Azam Pakpahan, Hansel Arie Pertiwi, Hanna Prasetyo, Lindo Pratita, Dian Galuh Pratomo, Adji Prihandi, Ifan Putra Perdana Prasetyo, Aditya Putri, Azizah Tiara Putri, Dina Putri, Envry Artanti Duidahayu Rahma, Siti Auliya Rahmad, Khozaeni Bin Rahmat, Bayu Pradana Nur Ramadani, Selika Fitrian Reza Maulana Rika Meliansyah Roedhy Poerwanto Rofiq, Muhamad Abdul Rossita Fauziah Rufaidah, Fathi Ruminta Ruminta Salamah, Raisha Nur Samsuryadi Samsuryadi Saputra, Muhammad Ajran Sarmayanta Sembiring Semendawai, Jaka Naufal Setiawan, Deris Shadiq, Jafar Sidabutar, Alex Onesimus SIska Rasiska, SIska Siti Julaeha, Siti Susanto Susanto Syamsul Arifin, M. Agus Syariful Mubarok Varinto, Irvan Waluyo, Nurmalita Wawan Sutari Wibawa, Rangga Widyastuti, R.A.D. Yanyan Mochamad Yani, Yanyan Mochamad Yaya Sudarya Triana Yazid Idris, Mohd. Yudho Suprapto, Bhakti Yulianto, Fiky Yusti Yusti, Yusti Zulhipni Reno Saputra Els